Human Error Prediction Using Heart Rate Variability and Electroencephalography
As human’s simple tasks are being increasingly replaced by autonomous systems and robots, it is likely that the responsibility of handling more complex tasks will be more often placed on human workers. Thus, situations in which workplace tasks change before human workers become proficient at those t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/23/9194 |
| _version_ | 1797462163237896192 |
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| author | Nahoko Takada Tipporn Laohakangvalvit Midori Sugaya |
| author_facet | Nahoko Takada Tipporn Laohakangvalvit Midori Sugaya |
| author_sort | Nahoko Takada |
| collection | DOAJ |
| description | As human’s simple tasks are being increasingly replaced by autonomous systems and robots, it is likely that the responsibility of handling more complex tasks will be more often placed on human workers. Thus, situations in which workplace tasks change before human workers become proficient at those tasks will arise more frequently due to rapid changes in business trends. Based on this background, the importance of preventing human error will become increasingly crucial. Existing studies on human error reveal how task errors are related to heart rate variability (HRV) indexes and electroencephalograph (EEG) indexes. However, in terms of preventing human error, analysis on their relationship with conditions before human error occurs (i.e., the human pre-error state) is still insufficient. This study aims at identifying biological indexes potentially useful for the detection of high-risk psychological states. As a result of correlation analysis between the number of errors in a Stroop task and the multiple HRV and EEG indexes obtained before and during the task, significant correlations were obtained with respect to several biological indexes. Specifically, we confirmed that conditions before the task are important for predicting the human error risk in high-cognitive-load tasks while conditions both before and during tasks are important in low-cognitive-load tasks. |
| first_indexed | 2024-03-09T17:32:35Z |
| format | Article |
| id | doaj.art-24be1a2ae5844a9895cd6a8a66015fd0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T17:32:35Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-24be1a2ae5844a9895cd6a8a66015fd02023-11-24T12:10:05ZengMDPI AGSensors1424-82202022-11-012223919410.3390/s22239194Human Error Prediction Using Heart Rate Variability and ElectroencephalographyNahoko Takada0Tipporn Laohakangvalvit1Midori Sugaya2Hitachi, Co., Ltd., Tokyo 319-1292, JapanShibaura Institute of Technology, Tokyo 135-8548, JapanShibaura Institute of Technology, Tokyo 135-8548, JapanAs human’s simple tasks are being increasingly replaced by autonomous systems and robots, it is likely that the responsibility of handling more complex tasks will be more often placed on human workers. Thus, situations in which workplace tasks change before human workers become proficient at those tasks will arise more frequently due to rapid changes in business trends. Based on this background, the importance of preventing human error will become increasingly crucial. Existing studies on human error reveal how task errors are related to heart rate variability (HRV) indexes and electroencephalograph (EEG) indexes. However, in terms of preventing human error, analysis on their relationship with conditions before human error occurs (i.e., the human pre-error state) is still insufficient. This study aims at identifying biological indexes potentially useful for the detection of high-risk psychological states. As a result of correlation analysis between the number of errors in a Stroop task and the multiple HRV and EEG indexes obtained before and during the task, significant correlations were obtained with respect to several biological indexes. Specifically, we confirmed that conditions before the task are important for predicting the human error risk in high-cognitive-load tasks while conditions both before and during tasks are important in low-cognitive-load tasks.https://www.mdpi.com/1424-8220/22/23/9194human errorheart rate variability (HRV)electroencephalograph (EEG)stroop task |
| spellingShingle | Nahoko Takada Tipporn Laohakangvalvit Midori Sugaya Human Error Prediction Using Heart Rate Variability and Electroencephalography Sensors human error heart rate variability (HRV) electroencephalograph (EEG) stroop task |
| title | Human Error Prediction Using Heart Rate Variability and Electroencephalography |
| title_full | Human Error Prediction Using Heart Rate Variability and Electroencephalography |
| title_fullStr | Human Error Prediction Using Heart Rate Variability and Electroencephalography |
| title_full_unstemmed | Human Error Prediction Using Heart Rate Variability and Electroencephalography |
| title_short | Human Error Prediction Using Heart Rate Variability and Electroencephalography |
| title_sort | human error prediction using heart rate variability and electroencephalography |
| topic | human error heart rate variability (HRV) electroencephalograph (EEG) stroop task |
| url | https://www.mdpi.com/1424-8220/22/23/9194 |
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